Brain–Computer Interfaces - Index of

296 C. Guger and G. Edlinger
Fig. 12 Left, mid panels: row/column speller. Right panel: single character speller
of course to different communication rates; with a 6 × 6 matrix, the row/column
approach increases speed by a factor of 6 (see also Chapter “BCIs in the Laboratory
and at Home: The Wadsworth Research Program” in this book).
The underlying phenomenon of a P300 speller is the P300 component of the
EEG, which is seen if an attended and relatively uncommon event occurs. The subject
must concentrate on a specific letter he wants to write [7, 11, 20]. When the
character flashes on, the P300 is induced and the maximum in the EEG amplitude
is reached typically 300 ms after the flash onset. Several repetitions are needed to
perform EEG data averaging to increase the signal to noise ratio and accuracy of the
system. The P300 signal response is more pronounced in the single character speller
than in the row/column speller and therefore easier to detect [7, 21].
For training, EEG data are acquired from the subject while the subject focuses
on the appearance of specific letters in the copy spelling mode. In this mode, an
arbitrary word like LUCAS is presented on the monitor. First, the subject counts
whenever the L flashes. Each row, column, or character flashes for e.g.100 ms per
flash. Then the subject counts the U until it flashes 15 times, and so on. These data,
together with the timing information of each flashing event, are then loaded into
g.BSanalyze. Then, the EEG data elicited by each flashing event are extracted within
a specific interval length and divided into sub-segments. The EEG data of each
segment are averaged and sent to a step-wise linear discriminant analysis (LDA).
The LDA is trained to separate the target characters, i.e. the characters the subject
was concentrating on (15 flashes × 5 characters), from all other events (15 × 36–
15 × 5). This yields again a subject specific WV for the real-time experiments. It
is very interesting for this approach that the LDA is trained only on 5 characters
representing 5 classes and not on all 36 classes. This is in contrast to the motor
imagery approach where each class must also be used as a training class. The P300
approach allows minimizing the time necessary for EEG recording for the setup
of the LDA. However, the accuracy of the spelling system increases also with the
number of training characters [21].
After the setup of the WV (same principle as in Fig. 10) real-time experiments
can be conducted with the Simulink model shown in Fig. 13.
The device driver “g.USBamp” reads again the EEG data from the amplifier and
converts the data to double precision. Then the data is band pass filtered (“Filter”)
to remove drifts and artifacts and down sampled to 64 Hz (Downsample 4:1’). The
“Control Flash” block generates the flashing sequence and the trigger signals for